Clinical Research Methodology

Aims and Objectives

Be able to understand different types of clinical research/epidemiology methodology

Understand the advantages and disadvantages of different clinical research/epidemiology methods

Use the appropriate clinical research/epidemiology method to investigate research questions

Clinical Research/Epidemiology

Normality/abnormalDiagnosisFrequencyRiskPrognosisTreatment

Steps in Clinical Research

ObservationAssociationCausationInterventionEvaluation

Cross-sectional study

Descriptive study or surveymeasure exposure and outcome in one

moment in timeexposure and outcome are measured

simultaneously

Cross-sectional study

Advantages quick and simple can study many

associations can estimate prevalence low participation/high

response possible to show validity

and reproducibility healthcare planning

Disadvantages problems with casualty survivor bias recall bias inefficient for rare diseases not suitable for disease of

short duration prevalence affected by low

response and migration in and out of population

Steps in Clinical Research

ObservationAssociationCausationInterventionEvaluation

Association and Causation

Association Causation eg earlobe crease and ischemic heart disease

Case control studyCohort study

Explanations for a Positive Association

BiasConfoundingChanceReverse CausationCausation

Bias

A systemic error introduced into the study by an investigator Result from design Just plain wrong

Two main types of bias Selection bias Information bias

Selection Bias

Occurs when selection of cases or control is related to exposure Selection of patients from hospitals, specialised

centres Selection of “healthy” controls from hospitals Response rate bias Self selection bias Survival bias

Selection Bias - example

Large scale study showed that “married” better survival than “widowed”

But … if widowed who remarried are reclassified as “married” and if illnesses reduce the chance of remarried, effect may be due to selection bias

Control from hospitals are more likely to have higher risk of smoking and high alcohol intake

Information Bias

Misclassification

Observer Bias

Recall Bias

Information Bias – Observer Bias

Observer know the underlying hypothesis and ask more probing question to those exposed than controls

Remedies Blind the observer Use highly structured interview

Information Bias - Recall Bias

Disease status affect patients’ response Patient with musculoskeletal diseases are more

likely to remember minor trauma

Particular problem with case control studiesRemedies

Find reliable records Use control with other illnesses

Confounding

Confounders confuse an association

A

C

B

Features of Confounding

Causal relationship between confounder and outcome

Confounder associated with outcomeNot simple on chain of causation

Eg depression smoking MI

Remedies to Confounding

Design Match (match case and control for gender and age) Restriction (limit study to certain groups) Randomisation (limit to treatment)

Analysis Stratification Standardisation Statistical modeling

Case Control Study

Retrospective study of previous exposureIdentify “Cases” and “Controls”Assess and compare the “exposure to risk”

in “Cases” and “Controls”eg smoking and lung cancer, HRT and

ischemic heart disease

Case Control Study

Advantages efficient for rare diseases relatively cheap and quick useful for long latency

periods useful for acute exposure

Disadvantages prone to bias difficulties in selecting

controls inefficient for rare

exposures cannot calculate incidence

rate temporal relationship may

not be clear

Cohort Study

Measures exposure then seek information on subsequent disease experience

PROSECTIVEAvoid bias provide large number are not

lost to follow upBUT don’t remove confounding

Steps in Clinical Research

ObservationAssociationCausationInterventionEvaluation

Intervention and Evaluation

Randomised controlled trial (RCTs)Not all risk can be tested in RCTs

sex smoking income

Clinical Effectiveness Efficacy in trials vs efficacy in real world

Costs, feasibility, acceptibility

Steps in Designing Clinical Trial

RationaleHypothesisType of trialPopulation studiedOutcome MeasuresNumber of casesAnalysis of results

Rationale for Trial

New drug does it work

Established drug are there new indications

Conventional therapy improving efficacy

Delivering care benefits of non-drug therapy

Rationale for Trial

The clinical problemThe burden of diseaseConventional therapy

Benefits Limitations

Rationale for trialStrong Common diseaseHigh health costsHigh social costsPoor current therapySignificant clinical

benefit likely

Weak Rare disease Limited morbidity Reasonable current

therapy Limited benefit

likely

Examples in Rheumatoid Arthritis

Does new analgesic reduce pain?

Does new slow-acting drug reduce inflammation?

Can combination therapy decrease joint damage in early disease?

Hypothesis

Simple

Testable

Relevant

Concise

An Example In RA

Does adding monthly

IM depot steroids to

conventional slow-acting drugs

reduce the number of patients

developing new erosions?

Type of Trial

Parallel Group

Factorial

Cross-over

n- of- 1

Parallel Groups

Cases RandomisedTreatment O

Treatment A

Cases Randomised

Treatment O

Treatment A

Treatment B

Factorial design

Cases RandomisedO A

B A+B

Cross-over Design

Cases Randomised

O A

A O

Selecting Cases (a)

Hospital / community

National/ international

Age, sex and general health

Activity, severity and duration

Select cases (b)Inclusion CriteriaDefinite casesKnown activityKnown durationInformed

Exclusion CriteriaYoung ElderlySickNon-responders

Selecting cases (c)

Hard Inclusion

Criteria

Soft Inclusion

Criteria

Less chance of response

Good generalisability

Rapid entry

Good chance response

Poor generalisability

Long period of entry

Randomised Controlled Trials (RCT)

Randomisation•Tossing a coin•Stratification

sexdisease durationgeographic areas

Blindness•To avoid placebo response•Levels of blindness

Patient blindAssessor blindTriple blind

•Code breaking

Outcome measures

Simple and reliableWidely acceptLikely to changeRelevant for hypothesisOne primary outcomeA limited number of outcomes

Core Data In RADemographic details

Age, sex, disease duration Social class and race

EULAR core data set Joint Counts and Pain Scores Acute Phase Measures (ESR) Function (HAQ) and X-rays (Larsen score)

Predictive factors Rheumatoid factor

Physician And Patient Assessments

Clinical OutcomesImpairment

Persisting synovitis

Functional Limitations in daily living

Radiological Anatomical joint damage

Patient centred Changes in lifestyle

Number of Cases: Sample Size

Calculations are conventional based on showing a difference of 5% significant at 90% power

Continuous discrete variables can be used Additional numbers should be included to

allow for dropouts*

Controls and Blinding

To avoid placebo responsePlacebo or not

placebo no treatmentLevels of blindness

Patient blind Assessor blind Triple blind

Code breaking

Significance and Power

Convention needs a difference with less than 5% of being due to chance

Convention suggests we should be 90% certain that the failure to find no difference between treatments is just due to chance.

Analysis (a)

Always planned in trial design

Usually after all patients completed study

Interim analysis are often misleading

Data dredging is unhelpful

Analysis (b)Potential cases

Did not fulfil entry criteriaGiven information

Did not consent

Randomised

Early withdrawal

Adverse effect

Lack of effect

Inter-current illness

OtherCompleted study

Analysis (c)

Difference must be significant in primary outcome measure for positive trial

Changes in secondary out comes provides supportive information or places therapy in context.